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TUES 24 OCT // 1:30 PM – 5:30 PM

Primary Content Focus: Measurement

Secondary Content Focus: Cross-Cutting

This hands-on workshop will introduce clinicians and researchers to building and comparing statistical models for longitudinal data using individual growth curve (IGC) analysis. IGC is an advanced data analysis technique that is extremely useful for anyone who works with data collected over time. IGC has real advantages over repeated measures ANOVA in terms of its flexibility (e.g., works even with missing data) and statistical power (e.g., test only the contrasts you are interested in), making it ideal for the clinical sciences.


  1. Import data into R and visualize data for longitudinal data analysis
  2. Construct multi-level statistical models allowing them to implement individual growth curve (IGC) analysis and test common hypotheses using these methods
  3. Effectively communicate their results and interpret the results of other researchers using multi-level models
  4. Communicate with a biostatistician/statistical consultant in the development of a multi-level modelling project (e.g., mutli-level hypotheses, assumptions, and tests)


Allan Kozlowski, PhD, BSc (PT)
Mary Free Bed Rehabilitation Hospital

Keith Lohse, PhD
Auburn University


Allan Kozlowski

Allan J. Kozlowski, PhD, BSc (PT), an expert in rehabilitation medicine, is Assistant Professor in the Department of Epidemiology and Biostatistics in the Michigan State University College of Human Medicine, and the Director of Outcomes Research in the John F. Butzer Center for Research & Innovation at Mary Free Bed Rehabilitation Hospital. The role is a joint appointment by Mary Free Bed and the College. He received his BSc in physical therapy in 1991 and his PhD in Rehabilitation Sciences in 2010, both from the University of British Columbia. He practiced as a physical therapist and rehabilitation manager in work disability prevention before completing his doctorate in Rehabilitation Sciences. Dr. Kozlowski completed his postdoctoral fellowship at the Center for Rehabilitation Outcomes Research at Rehabilitation Institute of Chicago and the Center for Healthcare Studies at Northwestern University, in which he constructed individual growth models for FIM Instrument scores for persons with spinal cord injuries and traumatic brain injuries. Prior to his current position, he expanded a powered exoskeleton research program examining device usability for persons with spinal cord injury and multiple sclerosis. In his current role, Dr. Kozlowski is leading an effort to model rehabilitation outcomes across post-acute care services for a variety of patient populations. Dr. Kozlowski has authored more than 20 articles on topics including modeling of rehabilitation outcomes as individual trajectories of change, psychometric properties of measurement instruments, and exoskeleton-assisted walking. He has also instructed courses in longitudinal modeling, measurement in clinical practice, and physical therapy clinical skills.

Keith Lohse received a joint PhD in neuroscience, cognitive science, and psychology from the University of Colorado and completed his post-doctoral training in rehabilitation science at the University of British Columbia. Dr. Lohse has more than 3o peer-reviewed manuscripts published in biomedical and psychology journals and has been invited to lead workshops on longitudinal data analysis. He also served as an ad-hoc reviewer for 24 scientific journals, and currently serves on the editorial board for the Journal of Motor Learning and Development. As the Director of the Rehabilitation Informatics Laboratory at Auburn University, Dr. Lohse and his team are exploring techniques for optimizing data collection, management, and analysis rehabilitation science and clinical practice. He has helped develop tools for the management and analysis of longitudinal data, and implemented large-scale meta-analytic research pooling data from hundreds of randomized controlled trials. Dr. Lohse has also pursued advanced training in statistical analysis and data science, specifically multi-level statistical models and their application to rehabilitation.


ACRM Annual ConferenceProgress in Rehabilitation Research (PIRR#2017)

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